{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 线性不可分数据\n",
    "X = np.c_[(.4, -.7),\n",
    "          (-1.5, -1),\n",
    "          (-1.4, -.9),\n",
    "          (-1.3, -1.2),\n",
    "          (-1.1, -.2),\n",
    "          (-1.2, -.4),\n",
    "          (-.5, 1.2),\n",
    "          (-1.5, 2.1),\n",
    "          (1, 1),\n",
    "          # --\n",
    "          (1.3, .8),\n",
    "          (1.2, .5),\n",
    "          (.2, -2),\n",
    "          (.5, -2.4),\n",
    "          (.2, -2.3),\n",
    "          (0, -2.7),\n",
    "          (1.3, 2.1)].T\n",
    "Y = [0] * 8 + [1] * 8 # 前8个一类，后8个一类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ca31ff1ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ca31ff1f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ca31fc5d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fignum = 1 # 表示第几幅图\n",
    "\n",
    "# 使用三种不同的核\n",
    "for kernel in ('linear', 'poly', 'rbf'):\n",
    "    clf = svm.SVC(kernel=kernel, gamma=2) # gamma=1/(2*σ^2)\n",
    "    clf.fit(X, Y)\n",
    "\n",
    "    plt.figure(fignum, figsize=(4, 3))\n",
    "    plt.clf()\n",
    "\n",
    "    # 支撑向量\n",
    "    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,\n",
    "                facecolors='none', zorder=10, edgecolors='k')\n",
    "    # 第一类\n",
    "    plt.scatter(X[:8, 0], X[:8, 1], c='g', zorder=10, cmap=plt.cm.Paired,\n",
    "                edgecolors='k')\n",
    "    # 第二类\n",
    "    plt.scatter(X[8:, 0], X[8:, 1], c='r', zorder=10, cmap=plt.cm.Paired,\n",
    "                edgecolors='k')\n",
    "\n",
    "    plt.axis('tight')\n",
    "    x_min = -3\n",
    "    x_max = 3\n",
    "    y_min = -3\n",
    "    y_max = 3\n",
    "\n",
    "    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] # 复数类似np.linspace\n",
    "    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n",
    "\n",
    "    # 绘制决策边界和间距\n",
    "    Z = Z.reshape(XX.shape)\n",
    "    plt.figure(fignum, figsize=(4, 3))\n",
    "    plt.contour(XX, YY, Z, colors=['g', 'r', 'g'], linestyles=['--', '-', '--'],\n",
    "                levels=[-.5, 0, .5]) # 为了图看着好看，间距设置成0.5\n",
    "\n",
    "    plt.xlim(x_min, x_max)\n",
    "    plt.ylim(y_min, y_max)\n",
    "\n",
    "    fignum = fignum + 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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